Au sujet de ce document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Piotr Szymański
- sl:arxiv_num : 1812.02956
- sl:arxiv_published : 2018-12-07T09:30:18Z
- sl:arxiv_summary : Multi-label classification aims to classify instances with discrete
non-exclusive labels. Most approaches on multi-label classification focus on
effective adaptation or transformation of existing binary and multi-class
learning approaches but fail in modelling the joint probability of labels or do
not preserve generalization abilities for unseen label combinations. To address
these issues we propose a new multi-label classification scheme, LNEMLC - Label
Network Embedding for Multi-Label Classification, that embeds the label network
and uses it to extend input space in learning and inference of any base
multi-label classifier. The approach allows capturing of labels' joint
probability at low computational complexity providing results comparable to the
best methods reported in the literature. We demonstrate how the method reveals
statistically significant improvements over the simple kNN baseline classifier.
We also provide hints for selecting the robust configuration that works
satisfactorily across data domains.@en
- sl:arxiv_title : LNEMLC: Label Network Embeddings for Multi-Label Classification@en
- sl:arxiv_updated : 2019-01-01T21:11:09Z
- sl:bookmarkOf : https://arxiv.org/abs/1812.02956
- sl:creationDate : 2020-08-12
- sl:creationTime : 2020-08-12T17:07:25Z
Documents with similar tags (experimental)
Préférences...